Increasing Interpretability of Bayesian Probabilistic Programming Models Through Interactive Representations

Increasing Interpretability of Bayesian Probabilistic Programming Models Through Interactive Representations

Bayesian probabilistic modeling is supported by powerful computational tools like probabilistic programming and efficient Markov Chain Monte Carlo (MCMC) sampling. However, the results of Bayesian inference are challenging for users to interpret in tasks like decision-making under uncertainty or mod...

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Bibliographic Details
Main Authors: Evdoxia Taka, Sebastian Stein, John H. Williamson
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-12-01
Series:Frontiers in Computer Science
Subjects:
Bayesian probabilistic modeling
Bayesian inference
probabilistic programming
Markov Chain Monte Carlo (MCMC)
uncertainty visualization
interactive visualization
Online Access:https://www.frontiersin.org/articles/10.3389/fcomp.2020.567344/full

Internet

https://www.frontiersin.org/articles/10.3389/fcomp.2020.567344/full

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